Faculty/School

Faculty of Science

School of Information Systems

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Supervisors

Associate Professor Chun Ouyang
Position
Associate Professor
Division / Faculty
Faculty of Science
Dr Roy Yang
Position
Postdoctoral Research Fellow
Division / Faculty
Faculty of Science

Overview

Process mining is an emerging discipline that aims at developing novel methods to discover knowledge from process execution data (stored in the format of event logs) and support evidence-based business process improvement. Specifically, process mining can be used for the task of discovering organizational models, providing insights on organizational structures and human resource (employee) performance. At the core of this task is cluster analysis, with challenges unique to process execution data.

Most state-of-the-art process mining techniques address this task through first learning resource features and then discovering resource clusters. However, with these two phases performed separately, such an approach introduces additional complexity and lacks in computational efficiency, hindering application of the techniques. A different approach to the problem is needed.

Research engagement

  • Literature review
  • Lab-based computational experiments
  • Report writing

Research activities

  • Literature review on cluster analysis techniques
  • Literature review on the key work of organizational model mining
  • Exploratory data analysis and data cleaning
  • Developing and testing computer algorithms

Outcomes

In this project, we will develop a novel algorithm to discover organizational models from event logs, based on the idea of biclustering. This algorithm is expected to simultaneously tackle the challenges of characterizing resource features and constructing resource clusters. It should be able to provide a robust solution to the problem, ensuring comparable effectiveness and high computational efficiency with respect to the state-of-the-art methods.

Skills and experience

  • Knowledge on data mining, specifically cluster analysis
  • Programming using Python and familiarity with the NumPy ecosystem
  • Knowledge on process mining and/or business process management is preferred
  • Solid skills for academic writing and communication

Start date

18 November, 2024

End date

21 February, 2025

Location

Gardens Point Campus

Keywords

Contact

Dr Jing (Roy) Yang

roy.j.yang@qut.edu.au